During late 2019 and early 2020, Australia experienced one of the most active bushfire seasons that advected large emissions over the adjacent ocean. Herein, we present a comprehensive research on mixed atmospheric aerosol particulate pollution emitted by wildfires in the atmosphere and the ocean. Based on a wide range of physical and biochemical data, including the Aerosol Robotic Network, multi-satellite observations, and Argo floats, we investigated the spatio-temporal variations and mixed compositions of aerosol particles, deposition in the coastal waters of eastern Australia and the South Pacific Ocean, and biogeochemical responses in the water column. Four types of wildfire-derived mixed particles were classified by using the optical properties of aerosols into four types, including the background aerosols, mineral dust, wildfire smoke particles, and residual smoke. The coarse particles accounted for more than 60% of the mineral dust on 22 November 2019 in the Tasman Sea; afterwards, during the wildfire smoke episode from December 2019 to January 2020, the particles affected large areas of the atmosphere such as eastern Australia, the South Pacific Ocean, and South America. The maximum value of the aerosol optical depth reached 2.74, and the proportion of fine particles accounted for 98.9% in the smoke episode. Mineral dust and smoke particles from the fire emissions changed the particle composition in the surface ocean. Particle deposition accounted for increases in chlorophyll-a concentration (Chla) standardized anomaly up to maximum of 23.3 with a lag time of less than 8 days. In the vertical direction, float observations showed the impact of exogenous particles on the water column could up to 64.7 m deep, resulting in Chla of 1.85 mg/m³. The high Chla lasted for a minimum period of two months until it returned to normal level.

Marine heatwaves (MHWs) represent anomalously warm temperature conditions of seawater that may affect marine life and ocean biogeochemistry. Under such conditions, phytoplankton communities may modify their structure and functions, and their resilience is not assured. This study characterizes the impact of MHWs on the phytoplankton spring bloom in the North‐Western Mediterranean Sea. Here, we synergistically combine autonomous observations from BioGeoChemical‐Argo floats, satellite‐based and marine ecosystem model data, and show that MHW events occurring during winter drastically inhibit phytoplankton carbon biomass in spring by up to 70%. Such reduction is related to the enhanced stratification of the water column under MHWs which hinders the renewal of nutrients from deep‐ocean reservoirs, thus preventing surface phytoplankton from blooming. This process negatively impacts particulate organic carbon stocks within the mixed layer, while severe events cause an earlier shift of phytoplankton phenology that provokes changes in zooplankton biomass distribution.

Phytoplankton species composition research is key to understanding phytoplankton ecological and biogeochemical functions. Hyperspectral optical sensor technology allows us to obtain detailed information about phytoplankton species composition. In the present study, a transfer learning method to inverse phytoplankton species composition using in situ hyperspectral remote sensing reflectance and hyperspectral satellite imagery was presented. By transferring the general knowledge learned from the first few layers of a deep neural network (DNN) trained by a general simulation dataset, and updating the last few layers with an in situ dataset, the requirement for large numbers of in situ samples for training the DNN to predict phytoplankton species composition in natural waters was lowered. This method was established from in situ datasets and validated with datasets collected in different ocean regions in China with considerable accuracy (R² = 0.88, mean absolute percentage error (MAPE) = 26.08%). Application of the method to Hyperspectral Imager for the Coastal Ocean (HICO) imagery showed that spatial distributions of dominant phytoplankton species and associated compositions could be derived. These results indicated the feasibility of species composition inversion from hyperspectral remote sensing, highlighting the advantages of transfer learning algorithms, which can bring broader application prospects for phytoplankton species composition and phytoplankton functional type research.

In estuarine-coastal-shelf seas, Particulate Organic Carbon (POC) shows the highest turnover rates of any organic carbon pool on the planet, playing a key role in the biological carbon pump. Compared with open ocean, estuarine and coastal waters are affected by large river inputs and show high hydrodynamic variability, which results in a mixture of diverse particles that includes inorganic mineral particles, living algal particles, and organic detritus. The highly complex and variable particle compositions in estuarine-coastal-shelf waters pose significant challenges in assessing their distinct roles in the carbon cycle and total POC. To overcome challenges, we collected biogeochemical and optical in situ data from 2014-2020 in estuarine-coastal-shelf waters of eastern China which is one of the largest estuarine-coastal-shelf systems in the world, to develop an algorithm that can optically discriminate particle composition and estimate their respective contributions to POC. The algorithm combines the quasi-analytical algorithm and the semi-empirical radiative transfer algorithm to estimate total suspended particle concentrations and the mass fraction of organic particles from which both phytoplankton- and detritus-related POC fractions are derived. Compared to existing POC algorithms, this algorithm shows improved retrievals compared to in situ counterparts, with r ² and RMSE values of 0.84 and 16.57 μg L ^-1 , respectively. The algorithm is also applied to Sentinel-3/OLCI images for the year of 2020. Applying particle component discrimination method can enhance our understanding of the roles of different particle composition in coastal carbon cycling affected by strong land-sea exchange.